Abstract
Metabolic analyses generally fall into two classes: unbiased metabolomic analyses and analyses that are targeted toward specific metabolites. Both techniques have been revolutionized by the advent of mass spectrometers with detectors that afford high mass accuracy and resolution, such as time-of-flights (TOFs) and Orbitraps. One particular area where this technology is key is in the field of metabolic flux analysis because the resolution of these spectrometers allows for discrimination between 13C-containing isotopologues and those containing 15N or other isotopes. While XCMS-based software is freely available for untargeted analysis of mass spectrometric data sets, it does not always identify metabolites of interest in a targeted assay. Furthermore, there is a paucity of vendor-independent software that deals with targeted analyses of metabolites and of isotopologues in particular. Here, we present AssayR, an R package that takes high resolution wide-scan liquid chromatography–mass spectrometry (LC-MS) data sets and tailors peak detection for each metabolite through a simple, iterative user interface. It automatically integrates peak areas for all isotopologues and outputs extracted ion chromatograms (EICs), absolute and relative stacked bar charts for all isotopologues, and a .csv data file. We demonstrate several examples where AssayR provides more accurate and robust quantitation than XCMS, and we propose that tailored peak detection should be the preferred approach for targeted assays. In summary, AssayR provides easy and robust targeted metabolite and stable isotope analyses on wide-scan data sets from high resolution mass spectrometers.
Highlights
Metabolic analyses generally fall into two classes: unbiased metabolomic analyses and analyses that are targeted toward specific metabolites
We demonstrate several examples where AssayR provides more accurate and robust quantitation than XCMS, and we propose that tailored peak detection should be the preferred approach for targeted assays
Some peaks are not found, with mixed mode hydrophilic liquid interaction (HILIC) chromatography where peaks can be broad and of irregular shape. This approach suffers serious limitation in the analysis of stable isotope tracing experiments because isotopologues are treated as distinct features during the peak detection stage when they should be detected in concert
Summary
We set several criteria for an ideal software tool that can take high resolution, high mass accuracy data from any mass spectrometer and return peak integrals for specific metabolites and their isotopologues. A configuration file in .tsv format is associated with each analysis (Figure 2) This file specifies the m/z value and the retention time (RT) window of each metabolite of interest as well as the maximum number of isotopologues to analyze (split into 13C, 15N, and 2H). Using the metabolomic data set from MRC5 primary human fibroblasts pulsed with 13C6glucose, we analyzed glycolytic and related metabolites with AssayR (Figure 4) and XCMS. Examination of the individual EICs and RT over which they were integrated revealed that XCMS had only picked part of the peak in sample 6 (red area of EIC), and this isotopologue was underrepresented in the analysis This type of error cannot occur in AssayR because. We present data that strongly support the use of tailored peak detection for the quantitation of specific metabolites in wide scan high resolution LC-MS data sets
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